def run_ga(n_to_test):
"""
This method specifies how to run the GA once the
initial random structures have been stored in godb.db.
"""
# Various initializations:
population_size = 10 # maximal size of the population
da = DataConnection('godb.db')
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize() # = [14] * 7
n_to_optimize = len(atom_numbers_to_optimize) # = 7
# This defines how close the Si atoms are allowed to get
# in candidate structures generated by the genetic operators:
blmin = closest_distances_generator(atom_numbers_to_optimize,
ratio_of_covalent_radii=0.4)
# This is our OFPComparator instance which will be
# used to judge whether or not two structures are identical:
comparator = OFPComparator(n_top=None, dE=1.0, cos_dist_max=1e-3,
rcut=10., binwidth=0.05, pbc=[False]*3,
sigma=0.1, nsigma=4, recalculate=False)
# Defining a typical combination of genetic operators:
pairing = CutAndSplicePairing(da.get_slab(), n_to_optimize, blmin)
rattlemut = RattleMutation(blmin, n_to_optimize, rattle_prop=0.8,
rattle_strength=1.5)
operators = OperationSelector([2., 1.], [pairing, rattlemut])
# Relax the randomly generated initial candidates:
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a = relax_one(a)
da.add_relaxed_step(a)
# Create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comparator,
logfile='log.txt')
current_pop = population.get_current_population()
# Test n_to_test new candidates
for step in range(n_to_test):
print('Starting configuration number %d' % step, flush=True)
a3 = None
while a3 is None:
a1, a2 = population.get_two_candidates()
a3, description = operators.get_new_individual([a1, a2])
da.add_unrelaxed_candidate(a3, description=description)
a3 = relax_one(a3)
da.add_relaxed_step(a3)
population.update()
best = population.get_current_population()[0]
print('Highest raw score at this point: %.3f' % get_raw_score(best))
print('GA finished after step %d' % step)
write('all_candidates.traj', da.get_all_relaxed_candidates())
write('current_population.traj', population.get_current_population())
a = da.get_an_unrelaxed_candidate()
relax(a)
da.add_relaxed_step(a)
# The structure comparator for the population
comp = OFPComparator(n_top=n_top, dE=1.0, cos_dist_max=5e-3, rcut=10.,
binwidth=0.05, pbc=[True, True, True], sigma=0.05,
nsigma=4, recalculate=False)
# The population
population = Population(data_connection=da,
population_size=10,
comparator=comp,
logfile='log.txt')
current_pop = population.get_current_population()
strainmut.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
pairing.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
# Test a few new candidates
n_to_test = 10
for step in range(n_to_test):
print('Now starting configuration number {0}'.format(step), flush=True)
# Generate a new candidate
a3 = None
while a3 is None:
a1, a2 = population.get_two_candidates()
a3, desc = operators.get_new_individual([a1, a2])
no_of_conn = list(get_parametrization(conf))
if no_of_conn not in sampled_points:
sampled_points.append(no_of_conn)
sampled_energies.append(conf.get_potential_energy())
sampled_points = np.array(sampled_points)
sampled_energies = np.array(sampled_energies)
if len(sampled_points) > 0 and len(sampled_energies) >= len(sampled_points[0]):
weights = np.linalg.lstsq(sampled_points, sampled_energies)[0]
else:
weights = None
# Submit new candidates until enough are running
while (not pbs_run.enough_jobs_running()
and len(population.get_current_population()) > 2):
a1, a2 = population.get_two_candidates()
# Selecting the "worst" parent energy
# which the child should be compared to
ce_a1 = da.get_atoms(a1.info['relax_id']).get_potential_energy()
ce_a2 = da.get_atoms(a2.info['relax_id']).get_potential_energy()
comparison_energy = min(ce_a1, ce_a2)
a3, desc = pairing.get_new_individual([a1, a2])
if a3 is None:
continue
if should_we_skip(a3, comparison_energy, weights):
continue
da.add_unrelaxed_candidate(a3, description=desc)
def run_ga(n_to_test, kptdensity=3.5):
population_size = 20
da = DataConnection('godb.db')
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize()
n_to_optimize = len(atom_numbers_to_optimize)
slab = da.get_slab()
all_atom_types = get_all_atom_types(slab, atom_numbers_to_optimize)
blmin = closest_distances_generator(all_atom_types, 0.05) # 0.5
# defining genetic operators:
mutation_probability = 0.75
pairing = CutAndSplicePairing(blmin,
p1=1.,
p2=0.,
minfrac=0.15,
use_tags=False)
cellbounds = CellBounds(
bounds={
'phi': [0.2 * 180., 0.8 * 180.],
'chi': [0.2 * 180., 0.8 * 180.],
'psi': [0.2 * 180., 0.8 * 180.]
})
strainmut = StrainMutation(blmin,
stddev=0.7,
cellbounds=cellbounds,
use_tags=False)
blmin_soft = closest_distances_generator(all_atom_types, 0.1)
softmut = SoftMutation(blmin_soft, bounds=[2., 5.], use_tags=False)
rattlemut = RattleMutation(blmin,
n_to_optimize,
rattle_prop=0.8,
rattle_strength=2.5,
use_tags=False)
mutations = OperationSelector([4., 4., 2], [softmut, strainmut, rattlemut])
if True:
# recalculate raw scores
structures = da.get_all_relaxed_candidates()
for atoms in structures:
atoms = singlepoint(atoms, kptdensity=kptdensity)
da.c.delete([atoms.info['relax_id']])
if 'data' not in atoms.info:
atoms.info['data'] = {}
da.add_relaxed_step(atoms)
print('Finished recalculating raw scores')
# relaxing the initial candidates:
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a.wrap()
a = relax_one(a, kptdensity=kptdensity)
da.add_relaxed_step(a)
# create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comparator,
logfile='log.txt')
current_pop = population.get_current_population()
strainmut.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
pairing.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
# Test n_to_test new candidates
ga_raw_scores = []
step = 0
for step in range(n_to_test):
print('Starting configuration number %d' % step, flush=True)
clock = time()
a3 = None
r = random()
if r > mutation_probability:
while a3 is None:
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
else:
while a3 is None:
a1 = population.get_one_candidate()
a3, desc = mutations.get_new_individual([a1])
dt = time() - clock
op = 'pairing' if r > mutation_probability else 'mutating'
print('Time for %s candidate(s): %.3f' % (op, dt), flush=True)
a3.wrap()
da.add_unrelaxed_candidate(a3, description=desc)
a3 = relax_one(a3, kptdensity=kptdensity)
da.add_relaxed_step(a3)
# Various updates:
population.update()
current_pop = population.get_current_population()
if step % 10 == 0:
strainmut.update_scaling_volume(current_pop,
w_adapt=0.5,
n_adapt=4)
pairing.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
write('current_population.traj', current_pop)
# Print out information for easy analysis/plotting afterwards:
if r > mutation_probability:
print('Step %d %s %.3f %.3f %.3f' % (step, desc,\
get_raw_score(a1), get_raw_score(a2), get_raw_score(a3)))
else:
print('Step %d %s %.3f %.3f' % (step, desc,\
get_raw_score(a1), get_raw_score(a3)))
print('Step %d highest raw score in pop: %.3f' % \
(step, get_raw_score(current_pop[0])))
ga_raw_scores.append(get_raw_score(a3))
print('Step %d highest raw score generated by GA: %.3f' % \
(step, max(ga_raw_scores)))
emin = population.pop[0].get_potential_energy()
print('GA finished after step %d' % step)
print('Lowest energy = %8.3f eV' % emin, flush=True)
write('all_candidates.traj', da.get_all_relaxed_candidates())
write('current_population.traj', population.get_current_population())
def run_ga(n_to_test, kptdensity=None):
''' This method specifies how to run the GA once the
initial random structures have been stored in godb.db.
'''
# Various initializations:
population_size = 10
da = DataConnection('godb.db')
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize()
n_to_optimize = len(atom_numbers_to_optimize)
slab = da.get_slab()
all_atom_types = get_all_atom_types(slab, atom_numbers_to_optimize)
blmin = closest_distances_generator(all_atom_types,
ratio_of_covalent_radii=0.05)
# Defining the mix of genetic operators:
mutation_probability = 0.3333
pairing = CutAndSplicePairing(slab, n_to_optimize, blmin)
rattlemut = RattleMutation(blmin, n_to_optimize,
rattle_prop=0.8, rattle_strength=1.5)
mirrormut = MirrorMutation(blmin, n_to_optimize)
mutations = OperationSelector([1., 1.], [rattlemut, mirrormut])
if True:
# Recalculate raw scores of any relaxed candidates
# present in the godb.db database (only applies to
# iter007).
structures = da.get_all_relaxed_candidates()
for atoms in structures:
atoms = singlepoint(atoms)
da.c.delete([atoms.info['relax_id']])
if 'data' not in atoms.info:
atoms.info['data'] = {}
da.add_relaxed_step(atoms)
print('Finished recalculating raw scores')
# Relax the randomly generated initial candidates:
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a.wrap()
a = relax_one(a)
da.add_relaxed_step(a)
# Create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comparator,
logfile='log.txt')
current_pop = population.get_current_population()
# Test n_to_test new candidates
ga_raw_scores = []
step = 0
for step in range(n_to_test):
print('Starting configuration number %d' % step, flush=True)
clock = time()
a3 = None
r = random()
if r > mutation_probability:
while a3 is None:
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
else:
while a3 is None:
a1 = population.get_one_candidate()
a3, desc = mutations.get_new_individual([a1])
dt = time() - clock
op = 'pairing' if r > mutation_probability else 'mutating'
print('Time for %s candidate(s): %.3f' % (op, dt), flush=True)
a3.wrap()
da.add_unrelaxed_candidate(a3, description=desc)
a3 = relax_one(a3)
da.add_relaxed_step(a3)
# Various updates:
population.update()
current_pop = population.get_current_population()
write('current_population.traj', current_pop)
# Print out information for easy analysis/plotting afterwards:
if r > mutation_probability:
print('Step %d %s %.3f %.3f %.3f' % (step, desc,\
get_raw_score(a1), get_raw_score(a2), get_raw_score(a3)))
else:
print('Step %d %s %.3f %.3f' % (step, desc,\
get_raw_score(a1), get_raw_score(a3)))
print('Step %d highest raw score in pop: %.3f' % \
(step, get_raw_score(current_pop[0])))
ga_raw_scores.append(get_raw_score(a3))
print('Step %d highest raw score generated by GA: %.3f' % \
(step, max(ga_raw_scores)))
emin = population.pop[0].get_potential_energy()
print('GA finished after step %d' % step)
print('Lowest energy = %8.3f eV' % emin, flush=True)
write('all_candidates.traj', da.get_all_relaxed_candidates())
write('current_population.traj', population.get_current_population())
PermutationMutation(n_to_optimize)])
# Relax all unrelaxed structures (e.g. the starting population)
while (da.get_number_of_unrelaxed_candidates() > 0 and
not pbs_run.enough_jobs_running()):
a = da.get_an_unrelaxed_candidate()
pbs_run.relax(a)
# create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comp)
# Submit new candidates until enough are running
while (not pbs_run.enough_jobs_running() and
len(population.get_current_population()) > 2):
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
if a3 is None:
continue
da.add_unrelaxed_candidate(a3, description=desc)
if random() < mutation_probability:
a3_mut, desc = mutations.get_new_individual([a3])
if a3_mut is not None:
da.add_unrelaxed_step(a3_mut, desc)
a3 = a3_mut
pbs_run.relax(a3)
write('all_candidates.traj', da.get_all_relaxed_candidates())
